Investigator interface and override functionality within compliance determination and enforcement platform

ABSTRACT

A compliance determination and enforcement platform is described. A plurality of factors are stored in association with each of a plurality of accounts. A factor entering module enters factors from each user account into a compliance score model. The compliance score model determines a compliance score for each one of the accounts based on the respective factors associated with the respective account. A comparator compares the compliance score for each account with a compliance reference score to determine a subset of the accounts that fail compliance and a subset of the accounts that meet compliance. A flagging unit flags the user accounts that fail compliance to indicate non-compliant accounts. A corrective action system allows for determining, for each one of the accounts that is flagged as non-compliant, whether the account is bad or good, entering the determination into a feedback system and closing the account.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a compliance determination and enforcementplatform and method.

2. Discussion of Related Art

Internet users frequently make use of intermediate transactionprocessors for purposes of making payments to other users or receivingpayments from other users. A transaction processor may have a data storewhere a user can open a user account. A transaction that is processedwith a transaction processor is recorded as a debit or a credit in thepaying and/or receiving user account.

Many users use their accounts for conducting legal activities, such aspaying for real or virtual goods or services. Other users may use theiraccounts for money laundering purposes or conducting other illicitactivities. An investigator may be able to determine whether an accountis being used for illicit activities by doing research on the parties ofthe transaction who receive or send payment and determining whether suchparties are regularly involved in illicit activities. It may for examplebe relatively easy to determine that a party sending or receivingpayment is in the business of conducting online services that may beillegal. In other cases, for example, where illegal goods or servicesare being exchanged outside of the Internet, it may not be practical orpossible for an investigator to determine that the parties are usingtheir accounts for illegal activities.

SUMMARY OF THE INVENTION

The invention provides a compliance determination and enforcementplatform including a processor, a computer-readable medium connected tothe processor and a set of computer readable code on thecomputer-readable medium. The set of computer readable code includes adata store, a plurality of user accounts stored in the data store, atransaction processor that is executable by the processor to processtransactions for the respective user accounts, a compliance referencescore stored in the data store, a plurality of factors stored inassociation with each account, a compliance score model, a factorentering module that is executable by the processor to enter at leastone factor from each user account into a compliance score model, whereinthe compliance score model is executable by the processor to determine acompliance score for each one of the accounts, wherein the respectivecompliance score for the respective account is based on the respectivefactor associated with the respective account, a comparator that isexecutable by the processor to compare the compliance score for eachaccount with the compliance reference score to determine a subset of theaccounts that fail compliance and a subset of the accounts that meetcompliance, a flagging unit that is executable by the processor to flagthe user accounts that fail compliance to indicate non-compliantaccounts and a corrective action system that is executable by theprocessor to perform a corrective action only for the accounts that areflagged as non-compliant accounts.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is an age of a user of the respective account.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is a level of due diligence that has been performed on therespective account.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is a balance of the respective account.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is a volume of transactions of the respective account.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is a geographical location of a user of the respectiveaccount.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is a number of devices used to access the respectiveaccount.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is one or more previous compliance reviews of the respectiveuser account.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is a based on if and how a user of the respective accounthas verified their identity.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is based on the transaction for the largest amount among thetransactions of the respective account.

The compliance determination and enforcement platform may furtherinclude that at least one of the factors entered into the compliancescore model is based on how many changes have been made to personaldetails of the respective account.

The compliance determination and enforcement platform may furtherinclude that the corrective action system allows for determining, foreach one of the accounts that is flagged as non-compliant, whether theaccount is bad or good. If the account is bad, then entering thedetermination that the account is bad into a feedback system and closingthe account and if the account is good, then entering the determinationthat the account is good into a feedback system without closing theaccount.

The compliance determination and enforcement platform may furtherinclude that the corrective action system allows for determining,whether a transaction in the account is for more than a predeterminedreporting amount and only if the transaction is for more than thepredetermined reporting amount, then filing a report.

The compliance determination and enforcement platform may furtherinclude a training set selector operable to select a training set of theuser accounts, the training set being a subset of the user accounts, atraining set flagging module operable to flag select ones of the useraccounts in the training set that fail compliance to indicatenon-compliant accounts to determine at least one fail parameter of thecon-compliant accounts in the training set, reference data establishedbased on the fail parameters, wherein the compliance score is based onthe reference data.

The compliance determination and enforcement platform may furtherinclude that the training set flagging module is operable determining aplurality of fail parameters of the con-compliant accounts in thetraining set, wherein the reference data is established based on theplurality of fail parameters.

The compliance determination and enforcement platform may furtherinclude that the compliance score model is executable by the processorto determine a compliance score for each one of the accounts other thanthe accounts of the training set, wherein the respective compliancescore for the respective account is based on the respective factorassociated with the respective account, wherein the respectivecompliance score is based on the reference data.

The compliance determination and enforcement platform may furtherinclude that a plurality of factors from each account are entered intothe compliance score model and the respective compliance score for therespective account is based on the respective plurality of factorsassociated with the respective account.

The compliance determination and enforcement platform may furtherinclude a corrective action storing unit storing the corrective actionin the data store, a model modifier, a corrective action transfer unitentering the corrective action into the model modifier, wherein themodel modifier is executable by the processor to modify the compliancescore model based on the corrective action.

The compliance determination and enforcement platform may furtherinclude that, after the compliance score module is modified, thetransaction processor is executable by the processor to processtransactions for the respective user accounts, the factor enteringmodule is executable by the processor to enter at least one factor fromeach user account into a compliance score model, the compliance scoremodel is executable by the processor to determine a compliance score foreach one of the accounts, wherein the respective compliance score forthe respective account is based on the respective factor associated withthe respective account, wherein the compliance score module calculates adifferent compliance score for a select account before the compliancescore module is modified than after the compliance score module ismodified, the comparator is executable by the processor to compare thecompliance score for each account with the compliance reference score todetermine a subset of the accounts that fail compliance and a subset ofthe accounts that meet compliance, the flagging unit that is executableby the processor to flag the user accounts that fail compliance toindicate non-compliant accounts and the corrective action system that isexecutable by the processor to perform a corrective action only for theaccounts that are flagged as non-compliant accounts.

The compliance determination and enforcement platform may furtherinclude a self learning knowledge repository, data stored in the selflearning knowledge repository, a self learning data entering moduleentering the data from the self learning knowledge repository in thecompliance score model, wherein the compliance score module calculates acompliance score for a select account based on the data from the selflearning knowledge repository, a self learning knowledge update moduleupdating the data in the self learning knowledge repository, wherein theself learning data entering module enters the data from the selflearning knowledge repository in the compliance score model after thedata in the self learning knowledge repository is updated, wherein thecompliance score module calculates a different compliance score for aselect account before the data in a self learning knowledge repositoryis updated than after the data in a self learning knowledge repositoryis updated.

The compliance determination and enforcement platform may furtherinclude that, after the compliance score module is modified, thetransaction processor is executable by the processor to processtransactions for the respective user accounts, the factor enteringmodule is executable by the processor to enter at least one factor fromeach user account into a compliance score model, the compliance scoremodel is executable by the processor to determine a compliance score foreach one of the accounts, wherein the respective compliance score forthe respective account is based on the respective factor associated withthe respective account, wherein the compliance score module calculates adifferent compliance score for a select account before the compliancescore module is modified than after the compliance score module ismodified, the comparator is executable by the processor to compare thecompliance score for each account with the compliance reference score todetermine a subset of the accounts that fail compliance and a subset ofthe accounts that meet compliance, the flagging unit that is executableby the processor to flag the user accounts that fail compliance toindicate non-compliant accounts and the corrective action system that isexecutable by the processor to perform a corrective action only for theaccounts that are flagged as non-compliant accounts.

The invention also provides a compliance determination and enforcementmethod including storing, by a processor, a plurality of user accountsin a data store, processing, by the processor, transactions for therespective user accounts, storing, by a processor, a compliancereference score in the data store, storing, by the processor, aplurality of factors in association with each account, entering, by theprocessor, at least one factor from each user account into a compliancescore model, executing, by the processor, the compliance score model todetermine a compliance score for each one of the accounts, wherein therespective compliance score for the respective account is based on therespective factor associated with the respective account, comparing, bythe processor, the compliance score for each account with the compliancereference score to determine a subset of the accounts that failcompliance and a subset of the accounts that meet compliance, flagging,by the processor, the user accounts that fail compliance to indicatenon-compliant accounts, executing, by the processor, a corrective actiononly for the accounts that are flagged as non-compliant accounts.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis an age of a user of the respective account.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis a level of due diligence that has been performed on the respectiveaccount.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis a balance of the respective account.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis a volume of transactions of the respective account.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis a geographical location of a user of the respective account.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis a number of devices used to access the respective account.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis one or more previous compliance reviews of the respective useraccount.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis a based on if and how a user of the respective account has verifiedtheir identity.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis based on the transaction for the largest amount among thetransactions of the respective account.

The compliance determination and enforcement method may further includethat at least one of the factors entered into the compliance score modelis based on how many changes have been made to personal details of therespective account.

The compliance determination and enforcement method may further includedetermining, for each one of the accounts that is flagged asnon-compliant, whether the account is bad or good. If the account isbad, then entering the determination that the account is bad into afeedback system and closing the account, and if the account is good,then entering the determination that the account is good into a feedbacksystem without closing the account.

The compliance determination and enforcement method may further includedetermining, whether a transaction in the account is for more than apredetermined reporting amount, and only if the transaction is for morethat the predetermined reporting amount, then filing a report.

The compliance determination and enforcement method may further includeselecting a training set of the user accounts, the training set being asubset of the user accounts, flagging select ones of the user accountsin the training set that fail compliance to indicate non-compliantaccounts, determining at least one fail parameter of the con-compliantaccounts in the training set and establishing reference data based onthe fail parameters, wherein the compliance score is based on thereference data.

The compliance determination and enforcement method may further includedetermining a plurality of fail parameters of the con-compliant accountsin the training set and establishing reference data based on theplurality of fail parameters.

The compliance determination and enforcement method may further includeexecuting, by the processor, the compliance score model to determine acompliance score for each one of the accounts other than the accounts ofthe training set, wherein the respective compliance score for therespective account is based on the respective factor associated with therespective account, wherein the respective compliance score is based onthe reference data.

The compliance determination and enforcement method may further includethat a plurality of factors from each account are entered into thecompliance score model and the respective compliance score for therespective account is based on the respective plurality of factorsassociated with the respective account.

The compliance determination and enforcement method may further includestoring, by the processor, the corrective action in the data store,entering, by the processor, the corrective action into a model modifierand executing, by the processor, the model modifier to modify thecompliance score model based on the corrective action.

The compliance determination and enforcement method may further includethat, after the compliance score module is modified, processing, by theprocessor, transactions for the respective user accounts after thecompliance score module is modified, entering, by the processor, atleast one factor from each user account into a compliance score model,executing, by the processor, the compliance score model to determine acompliance score for each one of the accounts, wherein the respectivecompliance score for the respective account is based on the respectivefactor associated with the respective account, wherein the compliancescore module calculates a different compliance score for a selectaccount before the compliance score module is modified than after thecompliance score module is modified, comparing, by the processor, thecompliance score for each account with the compliance reference score todetermine a subset of the accounts that fail compliance and a subset ofthe accounts that meet compliance, flagging, by the processor, the useraccounts that fail compliance to indicate non-compliant accounts andexecuting, by the processor, a corrective action only for the accountsthat are flagged as non-compliant accounts.

The compliance determination and enforcement method may further includestoring, by the processor, data in a self learning knowledge repository,entering, by the processor, the data from the self learning knowledgerepository in the compliance score model, wherein compliance scoremodule calculates a compliance score for a select account based on datafrom self learning knowledge repository, updating, by the processor, thedata in self learning knowledge repository, and entering, by theprocessor, the data from self learning knowledge repository in thecompliance score model after the data in the self learning knowledgerepository is updated, wherein the compliance score module calculates adifferent compliance score for a select account before the data in aself learning knowledge repository is updated than after the data in aself learning knowledge repository is updated.

The compliance determination and enforcement method may further includethat, after the compliance score module is modified, processing, by theprocessor, transactions for the respective user accounts after thecompliance score module is modified, entering, by the processor, atleast one factor from each user account into a compliance score model,executing, by the processor, the compliance score model to determine acompliance score for each one of the accounts, wherein the respectivecompliance score for the respective account is based on the respectivefactor associated with the respective account, wherein the compliancescore module calculates a different compliance score for a selectaccount before the compliance score module is modified than after thecompliance score module is modified, comparing, by the processor, thecompliance score for each account with the compliance reference score todetermine a subset of the accounts that fail compliance and a subset ofthe accounts that meet compliance, flagging, by the processor, the useraccounts that fail compliance to indicate non-compliant accounts andexecuting, by the processor, a corrective action only for the accountsthat are flagged as non-compliant accounts.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is further described by way of example with reference tothe accompanying drawings, wherein:

FIG. 1 is a block diagram of a compliance determination and enforcementplatform according to an embodiment of the invention;

FIGS. 2A and 2B show systems of the compliance determination andenforcement platform during initial training and continued operation,respectively;

FIG. 3 is a block diagram of the compliance determination andenforcement platform illustrating multi-factor integrated compliancedetermination and enforcement and investigator interface and overridefunctionality;

FIGS. 4A and 4B show a user interface that is provided by a correctiveaction system of the compliance determination and enforcement platform;

FIG. 5 is a flow chart illustrating a compliance determination andenforcement method;

FIG. 6 is a flow chart illustrating investigator interface and overridefunctionality of the compliance determination and enforcement method;

FIGS. 7A and 7B are block diagrams illustrating small sample basedtraining and large population application of a training system that isused in the compliance determination and enforcement platform;

FIG. 8 is a flow chart illustrating small sample based training andlarge population application;

FIG. 9 is a block diagram of the compliance determination andenforcement platform specifically illustrating a corrective actionrealignment and feedback system;

FIG. 10 is a flow chart illustrating compliance determination andenforcement with corrective action realignment and feedback;

FIG. 11 is a block diagram of the compliance determination andenforcement platform illustrating self learning subsystem integration;

FIG. 12 is a flow chart illustrating a compliance determination andenforcement method with integrated self learning; and

FIG. 13 is a block diagram of a machine in the form of a computer systemforming part of the compliance determination and enforcement platform.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 of the accompanying drawings illustrates a compliancedetermination and enforcement platform 10, according to an embodiment ofthe invention, including a compliance model core 12, a data store 14, afeedback system 16, a training system 18, a self learning system 20, adata retrieval channel 22, a transaction server 24, and a transactionprocessor 26.

The data store 14 includes user accounts 28, a compliance referencescore 30, and corrective actions 32.

The transaction server 24 is connected through the transaction processor26 to the user accounts 28 in the data store 14. The transactionprocessor 26 is executable to process transactions for the respectiveuser accounts 28. The transaction processor 26 receives transactionsfrom the transaction server 24 and enters the transactions into the useraccounts 28. A transaction can be a debit or a credit transaction andinclude other information such as a time and date stamp.

The compliance model core 12 receives data from the data store 14 andprovides data to the data store 14. The purpose of the compliance modelcore 12 is to flag select ones of the user accounts 28 as non-compliantaccounts and to provide an interface for investigators to take furthercorrective action.

The feedback system 16, training system 18, and self learning system 20are connected through the data retrieval channel 22 to the data store14. As shown in FIG. 2A, during initial training, the training system 18is executed. The training system 18 functions to train the compliancemodel core 12. The training system 18 receives data over the dataretrieval channel 22 from the data store 14. The feedback system 16 andthe self learning system 20 are not executed during initial training.

As shown in FIG. 2B, during continued operation, the feedback system 16,training system 18 and self learning system 20 are executed, typicallyin sequence after one another. All three systems 16, 18 and 20 receivedata over the data retrieval channel 22 from the data store 14. Thefeedback system 16 provides input into the training system 18. Thetraining system 18 then provides input into the self learning system 20.The self leaning system 20 provides input into the compliance model core12 for purposes of making adjustments to the compliance model core 12where necessary.

In FIG. 2A, the training system 18 uses a training data set of the useraccounts 28 that does not include all the user accounts 28. In FIG. 2B,the training system 18 receives data from all the user accounts 28.

FIG. 3 shows aspects of the compliance determination and enforcementplatform 10 as they relate to multi-factor integrated compliancedetermination and enforcement 31. The compliance model core 12 includesa factor entering module 33, a compliance score model 34, a comparator36, and a flagging unit 38.

The user account 28 includes multiple transactions, including atransaction 40. The transaction 40 includes a number of factors,including date and time 42, an amount 44, the other party in thetransaction 46, etc. The user account 28 further includes other factors48. The factor entering module 33 is a multi-factor entering module thatenters a plurality of the factors 48 in the user account 28 and factorsfrom the transaction 40. The factors that are received and entered bythe factor entering module 33 include, without limitation:

-   -   An age of a user of the user account 28;    -   A level of due diligence that has been performed on the user        account 28;    -   a balance of the user account 28;    -   recent volume of transactions of the user account 28 (buy, sell,        send, receive);    -   lifetime volume;    -   a geographical location of a user of the user account 28;    -   one or more previous compliance reviews of the user account 28;    -   if and how a user of the user account 28 has verified their        identity;    -   account created date;    -   email domain;    -   whether the account was restricted by fraud;    -   the highest balance to date?    -   whether the user is a merchant;    -   business type;    -   number of addresses on file;    -   number of bitcoin addresses transacted with;    -   number of bank accounts;    -   number of IPs;    -   number of phone numbers;    -   number of queues flagged in;    -   number of reviews by compliance;    -   number of unique counterparties;    -   number of verification attempts.

The factor entering module 33 is executable to enter the factors fromeach user account 28 into the compliance score model 34. The compliancescore model 34 is executable to determine a compliance score for eachone of the user accounts 28, with the respective compliance score forthe respective user account 28 being based on the plurality of factorsentered by the factor entering module 33. The compliance score model 34stores the compliance score for the user account 28 as a compliancescore 50 in relation to the user account 28.

The comparator 36 compares the compliance score 50 with the compliancereference score 30. By way of example, the comparator 36 determineswhether the compliance score 50 is more than the compliance referencescore 30 to determine whether the respective user account 28 should failcompliance, or should pass compliance if the compliance score 50 isequal to or less than the compliance reference score 30. The comparator36 then provides the result of the whether the respective user account28 fails or passes compliance to the flagging unit 38. The flagging unit38 then flags the respective user account 28 with a flag 52 indicatingthat the respective user account 28 either fails compliance or passescompliance. An account that fails compliance indicates that the accountis a non-compliant account. A non-compliant account may be an accountthat is suspected of being used for money laundering or other illicitactivities.

After each one of the user accounts 28 has been flagged as eithercompliant or non-compliant, the user accounts 28 can be divided into asubset of accounts that meet compliance 54 and a subset of the accountsthat fail compliance 56.

The compliance model core 12 further includes a corrective action system58. The corrective action system 58 takes further corrective action withrespect to the subset of the accounts that fail compliance 56. Thecorrective action system 58 includes components that are automaticallyexecuted and investigator-interactive user interfaces that allow forinvestigators to further analyze the accounts. The corrective actionsystem 58 is then used to take corrective action 60, where necessary,with respect to the user accounts 28 that are in the subset of useraccounts that fail compliance 56. The corrective action 60 may forexample be to close a user account 28 and to potentially file a report.

FIG. 4A shows an interface that is displayed to an investigator. Theinterface includes entries for a plurality of users. The compliancescore for each user is indicated in the right hand column. Theinvestigator has moused over the name of the second user, which allowsthe investigator view further information of the user account.

FIG. 4B shows the interface after the investigator has selected the link“More Actions” of one of the accounts. A drop-down list is displayedwhere the investigator selects an action item that is then stored inassociation with the account. The action items that are stored are thenused by the same investigator, another attendee or bycomputer-programmed code to execute further actions. Such furtheractions are displayed in the text of the drop-down list and may alsoinclude closing of the account. The action items that are stored arealso used by the feedback system 16 to further improve the compliancemodel core 12 (see FIG. 1). The action items may for example serve tovalidate/enhance the data that was used to flag the account. One of theaction items indicates that the account was incorrectly flagged (“Markas NOT useful”), which indicates that the account is compliant andprovides feedback to the system to indicate to the system that thesystem make modifications in the way that the accounts are scored sothat the account is not flagged with a high compliance score in thefuture, and other accounts are also not flagged with high compliancescores for similar factors or combinations of factors as the accountthat has been marked as being compliant.

FIG. 5 illustrates a compliance determination and enforcement method ashereinbefore described. At 70, a plurality of user accounts are storedin a data store. At 72, transactions are processed for the respectiveuser accounts. At 74, a compliance reference score is stored in the datastore. At 76, a plurality of factors are stored in association with eachaccount. At 78, the factors from each user account are entered into acompliance score model. At 80, the compliance score model is executed todetermine a compliance score for each one of the accounts. Therespective compliance score for the respective account is based on arespective factors associated with the respective account. At 82, theuser accounts that fail compliance are flagged to indicate non-compliantaccounts. At 84, corrective action is executed only for the accountsthat are flagged as non-compliant accounts.

FIG. 6 illustrates investigator interface and override functionality 90in more detail. At 92, an investigator determines whether an account isbad using the interface shown in FIG. 4. At 96, the investigator entersthe determination into the feedback system 16 shown in FIG. 1. Theinvestigator enters the determination into the feedback system 16irrespective of whether the account has been determined as being bad orgood.

If the investigator has determined that the account is good, then theprocess ends after 96.

If the investigator determines that the account is bad, then, at 98, theinvestigator closes the account or marks the account for more actions asdescribed with reference to FIG. 4B. Following 98, at 100, theinvestigator also determines whether the transaction that has resultedin non-compliance is larger than $2000 USD (a predetermined amount). Ifthe transaction is larger than $2000 USD, the investigator, at 102,considers filing a report with the relevant authorities. If thetransaction is less than $2000 USD, then the process ends without theinvestigator filing a report.

FIG. 7A illustrates small sample based training using the trainingsystem 18 in FIG. 1. The training system 18 includes a training setselector 110 and a training set flagging module 112. The training setselector 110 selects a training set 114 of the user accounts 28. Thetraining set 114 is a smaller sample of user accounts 28 than all of theuser accounts 28 and is used for initial training of the compliancescore model 34. By way of example, the training set selector 110 may beused to select a training set 114 of 5 percent or less of all the useraccounts 28. The training set selector 110 may be executed entirelyautomatically, or may include an interface for an operator to select theuser accounts 28 that form part of the training set 114.

The training set flagging module 112 flags selected ones of the useraccounts 28 of the training set 114 that fail compliance. In thismanner, the user accounts 28 of the training set 114 are identified asaccounts that meet compliance standards or accounts that fail compliancestandards. The training set flagging module 112 may be executed entirelyautomatically or an interface may be provided for an operator to selectthe accounts that fail compliance and for flagging accounts that failcompliance.

The training set flagging module 112 further determines a plurality offail parameters 115 based on the user accounts 28 that fail complianceand the user accounts that meet compliance in the training set 114. Byway of example, the fail parameters may be the factors that have beenidentified with reference to FIG. 3 above. The fail parameters 115 mayalso be weighted differently depending on their importance. The failparameters 115 are used to create reference data 116. The trainingsystem 18 enters the reference data 116 into the compliance score model34.

FIG. 7B illustrates large population application of the small samplebased training of FIG. 7A. The compliance score model 34 receivesfactors 48 from all the user accounts 28 that form part of the trainingset 114 and do not form part of the training set 114. The compliancescore model 34 then determines a compliance score of each one of theaccounts 28. The compliance score model 34 uses the reference data 116provided by the training system 18 in order to determine a compliancescore for each one of the user accounts 28 forming part of the trainingset 114 and the user accounts 28 not forming part of the training set114.

Small sample based training has the benefit of developing reference data116 relatively quickly for the compliance score model 34. To the extentthat an operator is involved, the operator only has to go through asmall number of user accounts for purposes of the training. Even thougha small number of accounts are analyzed during the initial training, thereference data 116 is still applicable to all the user accounts forpurposes of determining their compliance scores.

FIG. 8 illustrates a method of small sample based training and largepopulation application as it relates to compliance determination andenforcement. At 120, a training set of user accounts is selected. Thetraining set is a subset of the user accounts. At 122, flags are set forselect ones of the user accounts in the training set that failcompliance to indicate non-compliant accounts. At 124, one or morefailed parameters are determined of the non-compliant accounts in thetraining set. At 126, reference data is established based on the failedparameters. The compliance score (see FIG. 5:80) is based on thereference data.

FIG. 9 illustrates a corrective action and realignment and feedbacksystem 130 that is integrated into the compliance determination andenforcement platform 10.

As previously mentioned, the compliance model core 12 includes acorrective action system 58 that takes corrective action 60. Thefeedback system 16 includes a corrective action storing unit 132 that isconnected to the compliance model core 12. The corrective action storingunit 132 stores the corrective action 60 from the compliance model core12 in the data store 14 as the corrective action 32.

The feedback system 16 further includes a corrective action transferunit 134 connected to the data store 14, and a model modifier 136connected to the corrective action transfer unit 134. The correctiveaction transfer unit 134 retrieves the corrective action 32 from thedata store 14. The corrective action transfer unit 134 then enters thecorrective action 32 retrieved from the data store 14 to the modelmodifier 136. The model modifier 136 then modifies the compliance scoremodel 34 based on the corrective action 32.

The compliance score model 34 may previously have flagged an account asnon-compliant. After further investigation using the corrective actionsystem 58, an investigator may make the determination that the accountis compliant. The corrective action 60 will include that the account iscompliant and the account will not be closed. The corrective actionstoring unit 132 will store the corrective action 32 in the data store14 indicating that the account is compliant. The corrective actiontransfer unit 134 transfers the corrective action 32 to the modelmodifier 136 so that the model modifier 136 is informed of the accountthat has been determined to be compliant. The model modifier 136modifies the compliance score model 34 so that the compliance scoremodel 34, in future calculations, will determine that the account iscompliant. Further accounts that may previously have been flaggedbecause they have compliance scores that are non-compliant will, infuture, be flagged as having compliance scores that are compliant by thecompliance score model 34.

The compliance score model 34 is thus continuously updated with afeedback system 16 that is based on a human sanity check from thecorrective action system 58. The functioning of the compliance scoremodel 34 is continuously improved in this manner.

FIG. 10 illustrates a method of corrective action realignment andfeedback as part of the compliance determination and enforcement method.Step 70 to 84 have been described with reference to FIG. 5. At 150, thecorrective action is stored in the data store. At 152, the correctiveaction is entered into the model modifier. At 154, the model modifier isexecuted to modify the compliance score model based on the correctiveaction. At 156, the compliance score module calculates a compliancescore for each account. The compliance score for a select account isdifferent before the compliance score module is modified than after thecompliance score module is modified.

FIG. 11 shows self learning subsystem integration 170 within thecompliance determination and enforcement platform 10.

The self learning system 20 includes a self learning knowledge updatemodule 172 that is connected to the user accounts 28 in the data store14, a self learning knowledge repository 174, and a self learning dataentering module 176. The self learning knowledge update module 172 andthe self learning data entering module 176 are automatically executedsequentially after one another. The self learning knowledge repository174 had data 178 stored therein. The self learning knowledge updatemodule 172 continually retrieves data from the user accounts 28. Theself learning knowledge update module 172 continually assess weightfactors to apply to various data in the user accounts 28 for purposes ofcompliance scoring. The self learning knowledge update module 172provides the data together with their relative weight factors to theself learning knowledge repository 174, which stores the data as thedata 178. The data 178 is thus continually updated by the self learningknowledge update module 172. The data 178 may for example be updatedevery week or every month.

As soon as the data 178 has been updated, the self learning dataentering module 176 enters the data 178 into the compliance score model34. When the compliance score model 34 determines a compliance score fora respective user account as described with reference to FIG. 3, thecompliance score model 34 bases the compliance score on the datareceived from the self learning data entering module 176.

FIG. 12 illustrates a self learning method forming part of thecompliance determination and enforcement method. Steps 78 to 84 havebeen described with reference to FIG. 5. At 190, data is stored in theself learning knowledge repository. At 192, the data is entered from theself learning knowledge repository into the compliance score model. Whenthe compliance score model executes at 80, the compliance score modelbases the respective compliance scores for the respective accounts onthe data that is entered at 192.

At 194, further transactions are processed for the respective useraccounts. At 196, a plurality of factors are stored in association witheach account. The factors that are stored in association with eachaccount may be partially updated factors that have been stored asdescribed with reference to FIG. 3. At 198, the factors from the useraccounts are entered into the compliance score model as described withreference to FIG. 3. At 200, the compliance score model executes todetermine a compliance score for each one of the accounts. Therespective compliance score for the respective account is based on therespective factors associated with the respective account.

At 202, the data in the self learning knowledge repository is updated.At 204, the data from the self learning knowledge repository is enteredinto the compliance score model after the data in the self learningknowledge repository is updated. When the compliance score modelexecutes at 200, the compliance score module calculates a compliancescore for a select account based on data from the self learningknowledge repository. The compliance score module calculates a differentcompliance score for a select account before the data in the selflearning knowledge repository is updated (i.e., at 80) than after thedata in the self learning knowledge repository is updated (i.e., at202).

Following the execution of the compliance score model at 200, theprocess re-enters at 82 to flag user accounts that fail compliance.Following 204, the process re-enters at 200 to again update data in theself learning knowledge repository.

By continually updating the knowledge in the self learning knowledgerepository, the functioning of the compliance score model can beimproved over time. Factors can be added or deleted depending on theirrelevance for purposes of providing a compliance score. Factors that arerelevant for a compliance can be weighted differently over time toimprove the functioning of the compliance score model and improve therelevance of compliance scores.

FIG. 13 shows a diagrammatic representation of a machine in theexemplary form of a computer system 900 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a network deployment,the machine may operate in the capacity of a server or a client machinein a server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, anetwork router, switch or bridge, or any machine capable of executing aset of instructions (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The exemplary computer system 900 includes a processor 930 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), orboth), a main memory 932 (e.g., read-only memory (ROM), flash memory,dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) orRambus DRAM (RDRAM), etc.), and a static memory 934 (e.g., flash memory,static random access memory (SRAM, etc.), which communicate with eachother via a bus 936.

The computer system 900 may further include a video display 938 (e.g., aliquid crystal displays (LCD) or a cathode ray tube (CRT)). The computersystem 900 also includes an alpha-numeric input device 940 (e.g., akeyboard), a cursor control device 942 (e.g., a mouse), a disk driveunit 944, a signal generation device 946 (e.g., a speaker), and anetwork interface device 948.

The disk drive unit 944 includes a machine-readable medium 950 on whichis stored one or more sets of instructions 952 (e.g., software)embodying any one or more of the methodologies or functions describedherein. The software may also reside, completely or at least partially,within the main memory 932 and/or within the processor 930 duringexecution thereof by the computer system 900, the memory 932 and theprocessor 930 also constituting machine readable media. The software mayfurther be transmitted or received over a network 954 via the networkinterface device 948.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative and not restrictive of the current invention, andthat this invention is not restricted to the specific constructions andarrangements shown and described since modifications may occur to thoseordinarily skilled in the art.

What is claimed:
 1. A compliance determination and enforcement platformcomprising: a processor; a computer-readable medium connected to theprocessor; and a set of computer readable code on the computer-readablemedium, including: a data store; a plurality of user accounts stored inthe data store; a transaction processor that is executable by theprocessor to process transactions for the respective user accounts; acompliance reference score stored in the data store; a plurality offactors stored in association with each account; a trained compliancescore model that is trained by factors generated by the transactionprocessor; a factor entering module that is executable by the processorto enter at least one factor from each user account into the trainedcompliance score model, wherein the compliance score model is executableby the processor to determine a compliance score for each one of theaccounts, wherein the respective compliance score for the respectiveaccount is based on the respective at least one factor associated withthe respective account; a comparator that is executable by the processorto compare the compliance score for each account with the compliancereference score to determine a subset of the accounts that failcompliance and a subset of the accounts that meet compliance; a flaggingunit that is executable by the processor to flag the user accounts thatfail compliance to indicate non-compliant accounts; a corrective actionsystem that is executable by the processor to perform a correctiveaction only for the accounts that are flagged as non-compliant accounts,and to provide a user interface that displays a compliance score for atleast one of the plurality of accounts and receives user inputidentifying a selected flagged account as one of a bad account and agood account based on selection of an action, wherein the correctiveaction system is constructed to: in response to receiving user inputidentifying a flagged account as bad, enter information identifying theaccount as bad and the corrective action into a feedback system andclosing the account, and in response to receiving user input identifyinga flagged account as good, enter information identifying the account asgood into the feedback system without closing the account; and a modelmodifier unit that is executable by the processor to modify,automatically and without human input, the compliance score modelaccording to the information entered into the feedback system.
 2. Thecompliance determination and enforcement platform of claim 1, wherein atleast one of the factors entered into the compliance score model is anage of a user of the respective account.
 3. The compliance determinationand enforcement platform of claim 1, wherein at least one of the factorsentered into the compliance score model is a level of due diligence thathas been performed on the respective account.
 4. The compliancedetermination and enforcement platform of claim 1, wherein at least oneof the factors entered into the compliance score model is a balance ofthe respective account.
 5. The compliance determination and enforcementplatform of claim 1, wherein at least one of the factors entered intothe compliance score model is a volume of transactions of the respectiveaccount.
 6. The compliance determination and enforcement platform ofclaim 1, wherein at least one of the factors entered into the compliancescore model is a geographical location of a user of the respectiveaccount.
 7. The compliance determination and enforcement platform ofclaim 1, wherein at least one of the factors entered into the compliancescore model is a number of devices used to access the respectiveaccount.
 8. The compliance determination and enforcement platform ofclaim 1, wherein at least one of the factors entered into the compliancescore model is one or more previous compliance reviews of the respectiveuser account.
 9. The compliance determination and enforcement platformof claim 1, wherein at least one of the factors entered into thecompliance score model is a based on if and how a user of the respectiveaccount has verified their identity.
 10. The compliance determinationand enforcement platform of claim 1, wherein at least one of the factorsentered into the compliance score model is based on the transaction forthe largest amount among the transactions of the respective account. 11.The compliance determination and enforcement platform of claim 1,wherein at least one of the factors entered into the compliance scoremodel is based on how many changes have been made to personal details ofthe respective account.
 12. The compliance determination and enforcementplatform of claim 1, wherein the corrective action system allows for:determining, whether a transaction in the account is for more than apredetermined reporting amount; and only if the transaction is for morethan the predetermined reporting amount, then filing a report.
 13. Thecompliance determination and enforcement platform of claim 1, furthercomprising: a training set selector operable to select a training set ofthe user accounts, the training set being a subset of the user accounts;a training set flagging module operable to flag select ones of the useraccounts in the training set that fail compliance to indicatenon-compliant accounts to determine at least one fail parameter of thenon-compliant accounts in the training set; and reference dataestablished based on the fail parameters, wherein the compliance scoreis based on the reference data.
 14. The compliance determination andenforcement platform of claim 1, wherein a plurality of factors fromeach account are entered into the compliance score model and therespective compliance score for the respective account is based on therespective plurality of factors associated with the respective account.15. The compliance determination and enforcement platform of claim 1,further comprising: a corrective action storing unit storing thecorrective action in the data store; and a corrective action transferunit entering the corrective action into the model modifier unit. 16.The compliance determination and enforcement platform of claim 1,further comprising: a self learning knowledge repository; data stored inthe self learning knowledge repository; a self learning data enteringmodule entering the data from the self learning knowledge repository inthe compliance score model, wherein the compliance score modulecalculates a compliance score for a select account based on the datafrom the self learning knowledge repository; and a self learningknowledge update module updating the data in the self learning knowledgerepository, wherein the self learning data entering module enters thedata from the self learning knowledge repository in the compliance scoremodel after the data in the self learning knowledge repository isupdated, wherein the compliance score module calculates a differentcompliance score for a select account before the data in the selflearning knowledge repository is updated than after the data in the selflearning knowledge repository is updated.
 17. A compliance determinationand enforcement method, comprising: storing, by at least one processor,a plurality of user accounts in a data store; processing, by the atleast one processor, transactions for the respective user accounts;storing, by the at least one processor, a compliance reference score inthe data store; storing, by the at least one processor, a plurality offactors in association with each account; entering, by the at least oneprocessor, at least one factor from each user account into a trainedcompliance score model; executing, by the at least one processor, thecompliance score model to determine a compliance score for each one ofthe accounts, wherein the respective compliance score for the respectiveaccount is based on the at least one factor associated with therespective account; comparing, by the at least one processor, thecompliance score for each account with the compliance reference score todetermine a subset of the accounts that fail compliance and a subset ofthe accounts that meet compliance; flagging, by the at least oneprocessor, the user accounts that fail compliance to indicatenon-compliant accounts; executing, by the at least one processor, acorrective action only for the accounts that are flagged asnon-compliant accounts; receiving, by the at least one processor, userinput identifying a selected flagged account as one of a bad account anda good account based on selection of an action, wherein the user inputis received via a user interface that displays a compliance score for atleast one of the plurality of accounts; in response to receiving userinput that identifies the selected flagged account as a bad account,entering, by the at least one processor, information identifying theaccount as bad and the corrective action into a feedback system andclosing the account; in response to receiving user input that identifiesthe selected flagged account as a good account, entering, by the atleast one processor, information identifying the account as good intothe feedback system without closing the account; and modifying, by theat least one processor automatically and without human input, thecompliance score model according to the information entered into thefeedback system.